Hi Yik San,
You can check whether the execution environment used is `LocalStreamEnvironment` and you can get the class object corresponding to the corresponding java object through py4j in PyFlink. You can take a look at the example I wrote below, I hope it will help you
```
from pyflink.table import EnvironmentSettings, StreamTableEnvironment
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.java_gateway import get_gateway
from py4j.java_gateway import get_java_class
def test():
env = StreamExecutionEnvironment.get_execution_environment()
table_env = StreamTableEnvironment.create(
env, environment_settings=EnvironmentSettings.new_instance()
.in_streaming_mode().use_blink_planner().build())
gateway = get_gateway()
# get the execution environment class
env_class = table_env._j_tenv.getPlanner().getExecEnv().getClass()
# get the LocalStreamEnvironment class
local_stream_environment_class = get_java_class(
gateway.jvm.org.apache.flink.streaming.api.environment.LocalStreamEnvironment)
print(env_class == local_stream_environment_class)
if __name__ == '__main__':
test()
```
Hi,
> When executing jobs in mini cluster(e.g. when executing jobs in IDE) ... please remember to explicitly wait for the job execution to finish as these APIs are asynchronous.
I hope my program will be able to run in both local mode as well as in remote mode. Therefore I hope to do something like:
```python
result = ...
if local_mode:
result.wait()
else:
result
```
Is there a way to tell if the program is run under local mode vs. remote mode?
Best,
Yik San
Locked
